{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Important Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import sympy  as sp\n",
    "from sympy.matrices import Matrix\n",
    "from functools import partial\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "from math import pi\n",
    "import pprint\n",
    "pp=pprint.PrettyPrinter(indent=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Declaring Variables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "theta_i, alpha_i, d_i, a_i, A_i, a_3, d_1, d_3, d_5, d_7 = sp.symbols('theta_i alpha_i d_i a_i A_i a_3 d_1, d_3, d_5, d_7')\n",
    "theta_1,theta_2,theta_3,theta_4,theta_5,theta_6,theta_7 = sp.symbols ('theta_1,theta_2, theta_3, theta_4, theta_5, theta_6, theta_7')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Rotation and Translation Matrices\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Rotation Matrix for rotation about Z\n",
      "Matrix([\n",
      "[cos(theta_i), -sin(theta_i), 0, 0],\n",
      "[sin(theta_i),  cos(theta_i), 0, 0],\n",
      "[           0,             0, 1, 0],\n",
      "[           0,             0, 0, 1]])\n",
      "\n",
      "Rotation Matrix for rotation about X\n",
      "Matrix([\n",
      "[1,            0,             0, 0],\n",
      "[0, cos(alpha_i), -sin(alpha_i), 0],\n",
      "[0, sin(alpha_i),  cos(alpha_i), 0],\n",
      "[0,            0,             0, 1]])\n",
      "\n",
      "Translation Matrix for translation about Z\n",
      "Matrix([\n",
      "[1, 0, 0,   0],\n",
      "[0, 1, 0,   0],\n",
      "[0, 0, 1, d_i],\n",
      "[0, 0, 0,   1]])\n",
      "\n",
      "Translation Matrix for translation about X\n",
      "Matrix([\n",
      "[1, 0, 0, a_i],\n",
      "[0, 1, 0,   0],\n",
      "[0, 0, 1,   0],\n",
      "[0, 0, 0,   1]])\n"
     ]
    }
   ],
   "source": [
    "Rot_z = sp.Matrix([ [sp.cos(theta_i), -sp.sin(theta_i),0,0], [sp.sin(theta_i),sp.cos(theta_i),0,0], [0,0,1,0], [0,0,0,1] ]);\n",
    "Rot_x = sp.Matrix([ [1,0,0,0], [0,sp.cos(alpha_i), -sp.sin(alpha_i),0], [0, sp.sin(alpha_i), sp.cos(alpha_i), 0], [0,0,0,1] ]); \n",
    "Tran_z = sp.Matrix([[1,0,0,0], [0,1,0,0], [0,0,1,d_i], [0,0,0,1]]);\n",
    "Tran_x = sp.Matrix([[1,0,0,a_i], [0,1,0,0], [0,0,1,0], [0,0,0,1]]);\n",
    "print(\"\\nRotation Matrix for rotation about Z\")\n",
    "pp.pprint(Rot_z)\n",
    "print(\"\\nRotation Matrix for rotation about X\")\n",
    "pp.pprint(Rot_x)\n",
    "print(\"\\nTranslation Matrix for translation about Z\")\n",
    "pp.pprint(Tran_z)\n",
    "print(\"\\nTranslation Matrix for translation about X\")\n",
    "pp.pprint(Tran_x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### General Homogeneous Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix([\n",
      "[cos(theta_i), -sin(theta_i)*cos(alpha_i),  sin(alpha_i)*sin(theta_i), a_i*cos(theta_i)],\n",
      "[sin(theta_i),  cos(alpha_i)*cos(theta_i), -sin(alpha_i)*cos(theta_i), a_i*sin(theta_i)],\n",
      "[           0,               sin(alpha_i),               cos(alpha_i),              d_i],\n",
      "[           0,                          0,                          0,                1]])\n"
     ]
    }
   ],
   "source": [
    "A_i=Rot_z*Tran_z*Tran_x*Rot_x;\n",
    "pp.pprint(A_i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DH Parameter Table for Fixed $\\theta_3$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Link | $a_i$ | $\\theta_i$ | $\\alpha_i$ | $d_i$ |\n",
    "| --- | --- | --- | --- | --- |\n",
    "| 1 | 0 | $\\theta_1^*$ | 90 | $d_1$ |\n",
    "| 2 | 0 | $\\theta_2^*$ | -90 | 0 |\n",
    "| 3 | $a_3$ | 0 | -90 | $d_3$ |\n",
    "| 4 | $-a_3$ | $\\theta_4^*$ | 90 | 0 |\n",
    "| 5 | 0 | $\\theta_5^*$ | 90 | $d_5$ |\n",
    "| 6 | $a_3$ | $\\theta_6^*$ | -90 | 0 |\n",
    "| 7 | 0 | $\\theta_7^*$ | 0 | $-d_7$ |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Homogenous Matrix Ad0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}6.12323399573677 \\cdot 10^{-17} & 6.12323399573677 \\cdot 10^{-17} & -1.0 & 0\\\\-1.0 & 3.74939945665464 \\cdot 10^{-33} & -6.12323399573677 \\cdot 10^{-17} & 0\\\\0 & 1.0 & 6.12323399573677 \\cdot 10^{-17} & 0\\\\0 & 0 & 0 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[6.12323399573677e-17, 6.12323399573677e-17,                  -1.0, 0],\n",
       "[                -1.0, 3.74939945665464e-33, -6.12323399573677e-17, 0],\n",
       "[                   0,                  1.0,  6.12323399573677e-17, 0],\n",
       "[                   0,                    0,                     0, 1]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ad0=A_i.subs([(theta_i,math.radians(-90)),(alpha_i,math.radians(90)),(a_i,0),(d_i,0)])\n",
    "Ad0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Homogenous Matrix A1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}\\cos{\\left(\\theta_{1} \\right)} & - \\sin{\\left(\\theta_{1} \\right)} & 0 & 0\\\\\\sin{\\left(\\theta_{1} \\right)} & \\cos{\\left(\\theta_{1} \\right)} & 0 & 0\\\\0 & 0 & 1 & 0\\\\0 & 0 & 0 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[cos(theta_1), -sin(theta_1), 0, 0],\n",
       "[sin(theta_1),  cos(theta_1), 0, 0],\n",
       "[           0,             0, 1, 0],\n",
       "[           0,             0, 0, 1]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A1=A_i.subs([(theta_i,theta_1),(alpha_i,math.radians(0)),(a_i,0),(d_i,0)])\n",
    "A1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Homogenous Matrix Ad1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}6.12323399573677 \\cdot 10^{-17} & 6.12323399573677 \\cdot 10^{-17} & 1.0 & 0\\\\-1.0 & 3.74939945665464 \\cdot 10^{-33} & 6.12323399573677 \\cdot 10^{-17} & 0\\\\0 & -1.0 & 6.12323399573677 \\cdot 10^{-17} & 64.85\\\\0 & 0 & 0 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[6.12323399573677e-17, 6.12323399573677e-17,                  1.0,     0],\n",
       "[                -1.0, 3.74939945665464e-33, 6.12323399573677e-17,     0],\n",
       "[                   0,                 -1.0, 6.12323399573677e-17, 64.85],\n",
       "[                   0,                    0,                    0,     1]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ad1=A_i.subs([(theta_i,math.radians(-90)),(alpha_i,math.radians(-90)),(a_i,0),(d_i,64.85)])\n",
    "Ad1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Homogenous Matrix A2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}\\cos{\\left(\\theta_{2} \\right)} & - \\sin{\\left(\\theta_{2} \\right)} & 0 & 0\\\\\\sin{\\left(\\theta_{2} \\right)} & \\cos{\\left(\\theta_{2} \\right)} & 0 & 0\\\\0 & 0 & 1 & 140\\\\0 & 0 & 0 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[cos(theta_2), -sin(theta_2), 0,   0],\n",
       "[sin(theta_2),  cos(theta_2), 0,   0],\n",
       "[           0,             0, 1, 140],\n",
       "[           0,             0, 0,   1]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A2=A_i.subs([(theta_i,theta_2),(alpha_i,math.radians(0)),(a_i,0),(d_i,140)])\n",
    "A2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Homogenous Matrix Ad2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 0 & 0 & 500\\\\0 & 1 & 0 & 0\\\\0 & 0 & 1 & 0\\\\0 & 0 & 0 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1, 0, 0, 500],\n",
       "[0, 1, 0,   0],\n",
       "[0, 0, 1,   0],\n",
       "[0, 0, 0,   1]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ad2=A_i.subs([(theta_i,math.radians(0)),(alpha_i,math.radians(0)),(a_i,500),(d_i,0)])\n",
    "Ad2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Homogenous Matrix A3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}\\cos{\\left(\\theta_{3} \\right)} & - \\sin{\\left(\\theta_{3} \\right)} & 0 & 0\\\\\\sin{\\left(\\theta_{3} \\right)} & \\cos{\\left(\\theta_{3} \\right)} & 0 & 0\\\\0 & 0 & 1 & 0\\\\0 & 0 & 0 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[cos(theta_3), -sin(theta_3), 0, 0],\n",
       "[sin(theta_3),  cos(theta_3), 0, 0],\n",
       "[           0,             0, 1, 0],\n",
       "[           0,             0, 0, 1]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A3=A_i.subs([(theta_i,theta_3),(alpha_i,math.radians(0)),(a_i,0),(d_i,0)])\n",
    "A3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Homogenous Matrix A4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 0 & 0 & 450\\\\0 & 1 & 0 & 0\\\\0 & 0 & 1 & 0\\\\0 & 0 & 0 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1, 0, 0, 450],\n",
       "[0, 1, 0,   0],\n",
       "[0, 0, 1,   0],\n",
       "[0, 0, 0,   1]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A4=A_i.subs([(theta_i,0),(alpha_i,math.radians(0)),(a_i,450),(d_i,0)])\n",
    "A4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Steps to Get the Jacobian Matrix usinh Method 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Transformation Matrix  $T_e^0$ from $O_0$ to $O_e$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Matrix with joint angle set to required position\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}0 & 1.0 & 0 & -418.403390593274\\\\-6.12323399573677 \\cdot 10^{-17} & 0 & -1.0 & -140.0\\\\-1.0 & 0 & 6.12323399573677 \\cdot 10^{-17} & -803.553390593274\\\\0 & 0 & 0 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[                    0, 1.0,                    0, -418.403390593274],\n",
       "[-6.12323399573677e-17,   0,                 -1.0,            -140.0],\n",
       "[                 -1.0,   0, 6.12323399573677e-17, -803.553390593274],\n",
       "[                    0,   0,                    0,                 1]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T1=Ad0*A1\n",
    "T2=Ad0*A1*Ad1*A2\n",
    "T3=Ad0*A1*Ad1*A2*Ad2*A3\n",
    "T4=Ad0*A1*Ad1*A2*Ad2*A3*A4\n",
    "T = T4.subs([(theta_1,math.radians(0)),(theta_2,math.radians(-45)),(theta_3,math.radians(45))])\n",
    "print(\"Transformation Matrix with joint angle set to required position\")\n",
    "T\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating the Z vector for all links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Z0 matrix is given:\n",
      "Matrix([\n",
      "[                 -1.0],\n",
      "[-6.12323399573677e-17],\n",
      "[ 6.12323399573677e-17]])\n"
     ]
    }
   ],
   "source": [
    "print (\"The Z0 matrix is given:\")\n",
    "Z0 = T1[:3,2]\n",
    "pp.pprint(Z0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Z1 matrix is given:\n",
      "Matrix([\n",
      "[6.12323399573677e-17*sin(theta_1) + 6.12323399573677e-17*cos(theta_1) - 6.12323399573677e-17],\n",
      "[                 6.12323399573677e-17*sin(theta_1) - 1.0*cos(theta_1) - 3.74939945665464e-33],\n",
      "[                 1.0*sin(theta_1) + 6.12323399573677e-17*cos(theta_1) + 3.74939945665464e-33]])\n"
     ]
    }
   ],
   "source": [
    "print (\"The Z1 matrix is given:\")\n",
    "Z1 = T2[:3,2]\n",
    "pp.pprint(Z1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Z2 matrix is given:\n",
      "Matrix([\n",
      "[6.12323399573677e-17*sin(theta_1) + 6.12323399573677e-17*cos(theta_1) - 6.12323399573677e-17],\n",
      "[                 6.12323399573677e-17*sin(theta_1) - 1.0*cos(theta_1) - 3.74939945665464e-33],\n",
      "[                 1.0*sin(theta_1) + 6.12323399573677e-17*cos(theta_1) + 3.74939945665464e-33]])\n"
     ]
    }
   ],
   "source": [
    "print (\"The Z2 matrix is given:\")\n",
    "Z2 = T3[:3,2]\n",
    "pp.pprint(Z2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Z4 matrix is given:\n",
      "Matrix([\n",
      "[6.12323399573677e-17*sin(theta_1) + 6.12323399573677e-17*cos(theta_1) - 6.12323399573677e-17],\n",
      "[                 6.12323399573677e-17*sin(theta_1) - 1.0*cos(theta_1) - 3.74939945665464e-33],\n",
      "[                 1.0*sin(theta_1) + 6.12323399573677e-17*cos(theta_1) + 3.74939945665464e-33]])\n"
     ]
    }
   ],
   "source": [
    "print (\"The Z4 matrix is given:\")\n",
    "Z3 = T4[:3,2]\n",
    "pp.pprint(Z3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Forming the columns $J_1$ to $J_6$ of the Jacobian Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The initial jacobian matrix for home position is given by:\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}6.67432505535308 \\cdot 10^{-14} & 950.0 & 450.0\\\\-950.0 & 0 & 0\\\\140.0 & -5.81707229594993 \\cdot 10^{-14} & -2.75545529808154 \\cdot 10^{-14}\\\\-1.0 & 0 & 0\\\\-6.12323399573677 \\cdot 10^{-17} & -1.0 & -1.0\\\\6.12323399573677 \\cdot 10^{-17} & 6.12323399573677 \\cdot 10^{-17} & 6.12323399573677 \\cdot 10^{-17}\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[ 6.67432505535308e-14,                 950.0,                 450.0],\n",
       "[               -950.0,                     0,                     0],\n",
       "[                140.0, -5.81707229594993e-14, -2.75545529808154e-14],\n",
       "[                 -1.0,                     0,                     0],\n",
       "[-6.12323399573677e-17,                  -1.0,                  -1.0],\n",
       "[ 6.12323399573677e-17,  6.12323399573677e-17,  6.12323399573677e-17]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xp=T4[:3,3]\n",
    "diff_thet_1 = Xp.diff(theta_1) #Partially differentiating Xp wrt θ1\n",
    "\n",
    "diff_thet_2 = Xp.diff(theta_2) #Partially differentiating Xp wrt θ2\n",
    "\n",
    "diff_thet_3 = Xp.diff(theta_3) #Partially differentiating Xp wrt θ4\n",
    "\n",
    "\n",
    "print(\"The initial jacobian matrix for home position is given by:\")\n",
    "J = Matrix([[diff_thet_1[0],diff_thet_2[0],diff_thet_3[0]],\n",
    "          [diff_thet_1[1],diff_thet_2[1],diff_thet_3[1]],\n",
    "          [diff_thet_1[2],diff_thet_2[2],diff_thet_3[2]],\n",
    "          [Z0[0],Z1[0],Z2[0]],[Z0[1],Z1[1],Z2[1]],[Z0[2],Z1[2],Z2[2]]])\n",
    "\n",
    "J1=J.subs([(theta_1,0),(theta_2,0),(theta_3,0)])\n",
    "J1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Substitutng Link Lengths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}450 \\left(\\left(- 3.74939945665464 \\cdot 10^{-33} \\sin{\\left(\\theta_{1} \\right)} + 3.74939945665464 \\cdot 10^{-33} \\cos{\\left(\\theta_{1} \\right)}\\right) \\sin{\\left(\\theta_{2} \\right)} + \\left(6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} + 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)}\\right) \\cos{\\left(\\theta_{2} \\right)}\\right) \\cos{\\left(\\theta_{3} \\right)} + 450 \\left(\\left(- 3.74939945665464 \\cdot 10^{-33} \\sin{\\left(\\theta_{1} \\right)} + 3.74939945665464 \\cdot 10^{-33} \\cos{\\left(\\theta_{1} \\right)}\\right) \\cos{\\left(\\theta_{2} \\right)} - \\left(6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} + 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)}\\right) \\sin{\\left(\\theta_{2} \\right)}\\right) \\sin{\\left(\\theta_{3} \\right)} + 500 \\left(- 3.74939945665464 \\cdot 10^{-33} \\sin{\\left(\\theta_{1} \\right)} + 3.74939945665464 \\cdot 10^{-33} \\cos{\\left(\\theta_{1} \\right)}\\right) \\sin{\\left(\\theta_{2} \\right)} + 500 \\cdot \\left(6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} + 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)}\\right) \\cos{\\left(\\theta_{2} \\right)} - 8.57252759403147 \\cdot 10^{-15} \\sin{\\left(\\theta_{1} \\right)} + 8.57252759403147 \\cdot 10^{-15} \\cos{\\left(\\theta_{1} \\right)} & 450 \\left(- \\left(6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} - 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)}\\right) \\sin{\\left(\\theta_{2} \\right)} + \\left(3.74939945665464 \\cdot 10^{-33} \\sin{\\left(\\theta_{1} \\right)} + 3.74939945665464 \\cdot 10^{-33} \\cos{\\left(\\theta_{1} \\right)} + 1.0\\right) \\cos{\\left(\\theta_{2} \\right)}\\right) \\cos{\\left(\\theta_{3} \\right)} + 450 \\left(- \\left(6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} - 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)}\\right) \\cos{\\left(\\theta_{2} \\right)} - \\left(3.74939945665464 \\cdot 10^{-33} \\sin{\\left(\\theta_{1} \\right)} + 3.74939945665464 \\cdot 10^{-33} \\cos{\\left(\\theta_{1} \\right)} + 1.0\\right) \\sin{\\left(\\theta_{2} \\right)}\\right) \\sin{\\left(\\theta_{3} \\right)} - 500 \\cdot \\left(6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} - 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)}\\right) \\sin{\\left(\\theta_{2} \\right)} + 500 \\cdot \\left(3.74939945665464 \\cdot 10^{-33} \\sin{\\left(\\theta_{1} \\right)} + 3.74939945665464 \\cdot 10^{-33} \\cos{\\left(\\theta_{1} \\right)} + 1.0\\right) \\cos{\\left(\\theta_{2} \\right)} & 450 \\left(- \\left(6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} - 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)}\\right) \\sin{\\left(\\theta_{2} \\right)} + \\left(3.74939945665464 \\cdot 10^{-33} \\sin{\\left(\\theta_{1} \\right)} + 3.74939945665464 \\cdot 10^{-33} \\cos{\\left(\\theta_{1} \\right)} + 1.0\\right) \\cos{\\left(\\theta_{2} \\right)}\\right) \\cos{\\left(\\theta_{3} \\right)} - 450 \\left(\\left(6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} - 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)}\\right) \\cos{\\left(\\theta_{2} \\right)} + \\left(3.74939945665464 \\cdot 10^{-33} \\sin{\\left(\\theta_{1} \\right)} + 3.74939945665464 \\cdot 10^{-33} \\cos{\\left(\\theta_{1} \\right)} + 1.0\\right) \\sin{\\left(\\theta_{2} \\right)}\\right) \\sin{\\left(\\theta_{3} \\right)}\\\\450 \\left(- \\left(6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} - 1.0 \\cos{\\left(\\theta_{1} \\right)}\\right) \\sin{\\left(\\theta_{2} \\right)} + \\left(6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} + 3.74939945665464 \\cdot 10^{-33} \\cos{\\left(\\theta_{1} \\right)}\\right) \\cos{\\left(\\theta_{2} \\right)}\\right) \\sin{\\left(\\theta_{3} \\right)} + 450 \\left(\\left(6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} - 1.0 \\cos{\\left(\\theta_{1} \\right)}\\right) \\cos{\\left(\\theta_{2} \\right)} + \\left(6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} + 3.74939945665464 \\cdot 10^{-33} \\cos{\\left(\\theta_{1} \\right)}\\right) \\sin{\\left(\\theta_{2} \\right)}\\right) \\cos{\\left(\\theta_{3} \\right)} + 500 \\cdot \\left(6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} - 1.0 \\cos{\\left(\\theta_{1} \\right)}\\right) \\cos{\\left(\\theta_{2} \\right)} + 500 \\cdot \\left(6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} + 3.74939945665464 \\cdot 10^{-33} \\cos{\\left(\\theta_{1} \\right)}\\right) \\sin{\\left(\\theta_{2} \\right)} + 140.0 \\sin{\\left(\\theta_{1} \\right)} + 8.57252759403147 \\cdot 10^{-15} \\cos{\\left(\\theta_{1} \\right)} & 450 \\left(- \\left(- 1.0 \\sin{\\left(\\theta_{1} \\right)} - 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)}\\right) \\sin{\\left(\\theta_{2} \\right)} + \\left(3.74939945665464 \\cdot 10^{-33} \\sin{\\left(\\theta_{1} \\right)} - 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)} + 6.12323399573677 \\cdot 10^{-17}\\right) \\cos{\\left(\\theta_{2} \\right)}\\right) \\cos{\\left(\\theta_{3} \\right)} + 450 \\left(- \\left(- 1.0 \\sin{\\left(\\theta_{1} \\right)} - 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)}\\right) \\cos{\\left(\\theta_{2} \\right)} - \\left(3.74939945665464 \\cdot 10^{-33} \\sin{\\left(\\theta_{1} \\right)} - 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)} + 6.12323399573677 \\cdot 10^{-17}\\right) \\sin{\\left(\\theta_{2} \\right)}\\right) \\sin{\\left(\\theta_{3} \\right)} - 500 \\left(- 1.0 \\sin{\\left(\\theta_{1} \\right)} - 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)}\\right) \\sin{\\left(\\theta_{2} \\right)} + 500 \\cdot \\left(3.74939945665464 \\cdot 10^{-33} \\sin{\\left(\\theta_{1} \\right)} - 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)} + 6.12323399573677 \\cdot 10^{-17}\\right) \\cos{\\left(\\theta_{2} \\right)} & 450 \\left(- \\left(- 1.0 \\sin{\\left(\\theta_{1} \\right)} - 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)}\\right) \\sin{\\left(\\theta_{2} \\right)} + \\left(3.74939945665464 \\cdot 10^{-33} \\sin{\\left(\\theta_{1} \\right)} - 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)} + 6.12323399573677 \\cdot 10^{-17}\\right) \\cos{\\left(\\theta_{2} \\right)}\\right) \\cos{\\left(\\theta_{3} \\right)} - 450 \\left(\\left(- 1.0 \\sin{\\left(\\theta_{1} \\right)} - 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)}\\right) \\cos{\\left(\\theta_{2} \\right)} + \\left(3.74939945665464 \\cdot 10^{-33} \\sin{\\left(\\theta_{1} \\right)} - 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)} + 6.12323399573677 \\cdot 10^{-17}\\right) \\sin{\\left(\\theta_{2} \\right)}\\right) \\sin{\\left(\\theta_{3} \\right)}\\\\450 \\left(\\left(- 3.74939945665464 \\cdot 10^{-33} \\sin{\\left(\\theta_{1} \\right)} + 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)}\\right) \\sin{\\left(\\theta_{2} \\right)} + \\left(1.0 \\sin{\\left(\\theta_{1} \\right)} + 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)}\\right) \\cos{\\left(\\theta_{2} \\right)}\\right) \\cos{\\left(\\theta_{3} \\right)} + 450 \\left(\\left(- 3.74939945665464 \\cdot 10^{-33} \\sin{\\left(\\theta_{1} \\right)} + 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)}\\right) \\cos{\\left(\\theta_{2} \\right)} - \\left(1.0 \\sin{\\left(\\theta_{1} \\right)} + 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)}\\right) \\sin{\\left(\\theta_{2} \\right)}\\right) \\sin{\\left(\\theta_{3} \\right)} + 500 \\left(- 3.74939945665464 \\cdot 10^{-33} \\sin{\\left(\\theta_{1} \\right)} + 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)}\\right) \\sin{\\left(\\theta_{2} \\right)} + 500 \\cdot \\left(1.0 \\sin{\\left(\\theta_{1} \\right)} + 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)}\\right) \\cos{\\left(\\theta_{2} \\right)} - 8.57252759403147 \\cdot 10^{-15} \\sin{\\left(\\theta_{1} \\right)} + 140.0 \\cos{\\left(\\theta_{1} \\right)} & 450 \\left(- \\left(6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} - 1.0 \\cos{\\left(\\theta_{1} \\right)}\\right) \\sin{\\left(\\theta_{2} \\right)} + \\left(6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} + 3.74939945665464 \\cdot 10^{-33} \\cos{\\left(\\theta_{1} \\right)} - 6.12323399573677 \\cdot 10^{-17}\\right) \\cos{\\left(\\theta_{2} \\right)}\\right) \\cos{\\left(\\theta_{3} \\right)} + 450 \\left(- \\left(6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} - 1.0 \\cos{\\left(\\theta_{1} \\right)}\\right) \\cos{\\left(\\theta_{2} \\right)} - \\left(6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} + 3.74939945665464 \\cdot 10^{-33} \\cos{\\left(\\theta_{1} \\right)} - 6.12323399573677 \\cdot 10^{-17}\\right) \\sin{\\left(\\theta_{2} \\right)}\\right) \\sin{\\left(\\theta_{3} \\right)} - 500 \\cdot \\left(6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} - 1.0 \\cos{\\left(\\theta_{1} \\right)}\\right) \\sin{\\left(\\theta_{2} \\right)} + 500 \\cdot \\left(6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} + 3.74939945665464 \\cdot 10^{-33} \\cos{\\left(\\theta_{1} \\right)} - 6.12323399573677 \\cdot 10^{-17}\\right) \\cos{\\left(\\theta_{2} \\right)} & 450 \\left(- \\left(6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} - 1.0 \\cos{\\left(\\theta_{1} \\right)}\\right) \\sin{\\left(\\theta_{2} \\right)} + \\left(6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} + 3.74939945665464 \\cdot 10^{-33} \\cos{\\left(\\theta_{1} \\right)} - 6.12323399573677 \\cdot 10^{-17}\\right) \\cos{\\left(\\theta_{2} \\right)}\\right) \\cos{\\left(\\theta_{3} \\right)} - 450 \\left(\\left(6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} - 1.0 \\cos{\\left(\\theta_{1} \\right)}\\right) \\cos{\\left(\\theta_{2} \\right)} + \\left(6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} + 3.74939945665464 \\cdot 10^{-33} \\cos{\\left(\\theta_{1} \\right)} - 6.12323399573677 \\cdot 10^{-17}\\right) \\sin{\\left(\\theta_{2} \\right)}\\right) \\sin{\\left(\\theta_{3} \\right)}\\\\-1.0 & 6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} + 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)} - 6.12323399573677 \\cdot 10^{-17} & 6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} + 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)} - 6.12323399573677 \\cdot 10^{-17}\\\\-6.12323399573677 \\cdot 10^{-17} & 6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} - 1.0 \\cos{\\left(\\theta_{1} \\right)} - 3.74939945665464 \\cdot 10^{-33} & 6.12323399573677 \\cdot 10^{-17} \\sin{\\left(\\theta_{1} \\right)} - 1.0 \\cos{\\left(\\theta_{1} \\right)} - 3.74939945665464 \\cdot 10^{-33}\\\\6.12323399573677 \\cdot 10^{-17} & 1.0 \\sin{\\left(\\theta_{1} \\right)} + 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)} + 3.74939945665464 \\cdot 10^{-33} & 1.0 \\sin{\\left(\\theta_{1} \\right)} + 6.12323399573677 \\cdot 10^{-17} \\cos{\\left(\\theta_{1} \\right)} + 3.74939945665464 \\cdot 10^{-33}\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[450*((-3.74939945665464e-33*sin(theta_1) + 3.74939945665464e-33*cos(theta_1))*sin(theta_2) + (6.12323399573677e-17*sin(theta_1) + 6.12323399573677e-17*cos(theta_1))*cos(theta_2))*cos(theta_3) + 450*((-3.74939945665464e-33*sin(theta_1) + 3.74939945665464e-33*cos(theta_1))*cos(theta_2) - (6.12323399573677e-17*sin(theta_1) + 6.12323399573677e-17*cos(theta_1))*sin(theta_2))*sin(theta_3) + 500*(-3.74939945665464e-33*sin(theta_1) + 3.74939945665464e-33*cos(theta_1))*sin(theta_2) + 500*(6.12323399573677e-17*sin(theta_1) + 6.12323399573677e-17*cos(theta_1))*cos(theta_2) - 8.57252759403147e-15*sin(theta_1) + 8.57252759403147e-15*cos(theta_1),    450*(-(6.12323399573677e-17*sin(theta_1) - 6.12323399573677e-17*cos(theta_1))*sin(theta_2) + (3.74939945665464e-33*sin(theta_1) + 3.74939945665464e-33*cos(theta_1) + 1.0)*cos(theta_2))*cos(theta_3) + 450*(-(6.12323399573677e-17*sin(theta_1) - 6.12323399573677e-17*cos(theta_1))*cos(theta_2) - (3.74939945665464e-33*sin(theta_1) + 3.74939945665464e-33*cos(theta_1) + 1.0)*sin(theta_2))*sin(theta_3) - 500*(6.12323399573677e-17*sin(theta_1) - 6.12323399573677e-17*cos(theta_1))*sin(theta_2) + 500*(3.74939945665464e-33*sin(theta_1) + 3.74939945665464e-33*cos(theta_1) + 1.0)*cos(theta_2),   450*(-(6.12323399573677e-17*sin(theta_1) - 6.12323399573677e-17*cos(theta_1))*sin(theta_2) + (3.74939945665464e-33*sin(theta_1) + 3.74939945665464e-33*cos(theta_1) + 1.0)*cos(theta_2))*cos(theta_3) - 450*((6.12323399573677e-17*sin(theta_1) - 6.12323399573677e-17*cos(theta_1))*cos(theta_2) + (3.74939945665464e-33*sin(theta_1) + 3.74939945665464e-33*cos(theta_1) + 1.0)*sin(theta_2))*sin(theta_3)],\n",
       "[                                                                    450*(-(6.12323399573677e-17*sin(theta_1) - 1.0*cos(theta_1))*sin(theta_2) + (6.12323399573677e-17*sin(theta_1) + 3.74939945665464e-33*cos(theta_1))*cos(theta_2))*sin(theta_3) + 450*((6.12323399573677e-17*sin(theta_1) - 1.0*cos(theta_1))*cos(theta_2) + (6.12323399573677e-17*sin(theta_1) + 3.74939945665464e-33*cos(theta_1))*sin(theta_2))*cos(theta_3) + 500*(6.12323399573677e-17*sin(theta_1) - 1.0*cos(theta_1))*cos(theta_2) + 500*(6.12323399573677e-17*sin(theta_1) + 3.74939945665464e-33*cos(theta_1))*sin(theta_2) + 140.0*sin(theta_1) + 8.57252759403147e-15*cos(theta_1), 450*(-(-1.0*sin(theta_1) - 6.12323399573677e-17*cos(theta_1))*sin(theta_2) + (3.74939945665464e-33*sin(theta_1) - 6.12323399573677e-17*cos(theta_1) + 6.12323399573677e-17)*cos(theta_2))*cos(theta_3) + 450*(-(-1.0*sin(theta_1) - 6.12323399573677e-17*cos(theta_1))*cos(theta_2) - (3.74939945665464e-33*sin(theta_1) - 6.12323399573677e-17*cos(theta_1) + 6.12323399573677e-17)*sin(theta_2))*sin(theta_3) - 500*(-1.0*sin(theta_1) - 6.12323399573677e-17*cos(theta_1))*sin(theta_2) + 500*(3.74939945665464e-33*sin(theta_1) - 6.12323399573677e-17*cos(theta_1) + 6.12323399573677e-17)*cos(theta_2), 450*(-(-1.0*sin(theta_1) - 6.12323399573677e-17*cos(theta_1))*sin(theta_2) + (3.74939945665464e-33*sin(theta_1) - 6.12323399573677e-17*cos(theta_1) + 6.12323399573677e-17)*cos(theta_2))*cos(theta_3) - 450*((-1.0*sin(theta_1) - 6.12323399573677e-17*cos(theta_1))*cos(theta_2) + (3.74939945665464e-33*sin(theta_1) - 6.12323399573677e-17*cos(theta_1) + 6.12323399573677e-17)*sin(theta_2))*sin(theta_3)],\n",
       "[                                                                  450*((-3.74939945665464e-33*sin(theta_1) + 6.12323399573677e-17*cos(theta_1))*sin(theta_2) + (1.0*sin(theta_1) + 6.12323399573677e-17*cos(theta_1))*cos(theta_2))*cos(theta_3) + 450*((-3.74939945665464e-33*sin(theta_1) + 6.12323399573677e-17*cos(theta_1))*cos(theta_2) - (1.0*sin(theta_1) + 6.12323399573677e-17*cos(theta_1))*sin(theta_2))*sin(theta_3) + 500*(-3.74939945665464e-33*sin(theta_1) + 6.12323399573677e-17*cos(theta_1))*sin(theta_2) + 500*(1.0*sin(theta_1) + 6.12323399573677e-17*cos(theta_1))*cos(theta_2) - 8.57252759403147e-15*sin(theta_1) + 140.0*cos(theta_1),    450*(-(6.12323399573677e-17*sin(theta_1) - 1.0*cos(theta_1))*sin(theta_2) + (6.12323399573677e-17*sin(theta_1) + 3.74939945665464e-33*cos(theta_1) - 6.12323399573677e-17)*cos(theta_2))*cos(theta_3) + 450*(-(6.12323399573677e-17*sin(theta_1) - 1.0*cos(theta_1))*cos(theta_2) - (6.12323399573677e-17*sin(theta_1) + 3.74939945665464e-33*cos(theta_1) - 6.12323399573677e-17)*sin(theta_2))*sin(theta_3) - 500*(6.12323399573677e-17*sin(theta_1) - 1.0*cos(theta_1))*sin(theta_2) + 500*(6.12323399573677e-17*sin(theta_1) + 3.74939945665464e-33*cos(theta_1) - 6.12323399573677e-17)*cos(theta_2),   450*(-(6.12323399573677e-17*sin(theta_1) - 1.0*cos(theta_1))*sin(theta_2) + (6.12323399573677e-17*sin(theta_1) + 3.74939945665464e-33*cos(theta_1) - 6.12323399573677e-17)*cos(theta_2))*cos(theta_3) - 450*((6.12323399573677e-17*sin(theta_1) - 1.0*cos(theta_1))*cos(theta_2) + (6.12323399573677e-17*sin(theta_1) + 3.74939945665464e-33*cos(theta_1) - 6.12323399573677e-17)*sin(theta_2))*sin(theta_3)],\n",
       "[                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            -1.0,                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 6.12323399573677e-17*sin(theta_1) + 6.12323399573677e-17*cos(theta_1) - 6.12323399573677e-17,                                                                                                                                                                                                                                                                                                                   6.12323399573677e-17*sin(theta_1) + 6.12323399573677e-17*cos(theta_1) - 6.12323399573677e-17],\n",
       "[                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           -6.12323399573677e-17,                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  6.12323399573677e-17*sin(theta_1) - 1.0*cos(theta_1) - 3.74939945665464e-33,                                                                                                                                                                                                                                                                                                                                    6.12323399573677e-17*sin(theta_1) - 1.0*cos(theta_1) - 3.74939945665464e-33],\n",
       "[                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            6.12323399573677e-17,                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  1.0*sin(theta_1) + 6.12323399573677e-17*cos(theta_1) + 3.74939945665464e-33,                                                                                                                                                                                                                                                                                                                                    1.0*sin(theta_1) + 6.12323399573677e-17*cos(theta_1) + 3.74939945665464e-33]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "J=J.subs([(d_1,0.3330),(d_3,0.3160),(d_5,0.3840),(d_7,0.2070),(a_3,0.0880)]);J"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Circle equations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The equation of ciecle is given by\n",
    "$y^2+(z-72.5)^2=100$  \n",
    "Using the polar form  \n",
    "$y=r\\cos(\\theta),z=r\\sin(\\theta)$  \n",
    "Therefore,  \n",
    "$\\dot{y}=r\\cos(\\theta)\\dot{\\theta}$  \n",
    "$\\dot{z}=r\\sin(\\theta)\\dot{\\theta}$\n",
    "\n",
    "Also, $\\dot{\\theta}=\\frac{2\\pi}{5}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}0.717356090899523 & 0.696706709347165 & 0 & -64.1486027140603\\\\-4.26609820773246 \\cdot 10^{-17} & 4.39253920284479 \\cdot 10^{-17} & -1.0 & -140.0\\\\-0.696706709347165 & 0.717356090899523 & 6.12323399573677 \\cdot 10^{-17} & -695.939112848469\\\\0 & 0 & 0 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[    0.717356090899523,    0.696706709347165,                    0, -64.1486027140603],\n",
       "[-4.26609820773246e-17, 4.39253920284479e-17,                 -1.0,            -140.0],\n",
       "[   -0.696706709347165,    0.717356090899523, 6.12323399573677e-17, -695.939112848469],\n",
       "[                    0,                    0,                    0,                 1]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T=T4\n",
    "T_eval=T.subs([(theta_1,0),(theta_2,-0.7),(theta_3,1.5)])\n",
    "# T_eval.simplify()\n",
    "T_eval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using the above equations and the Jacobian matrix we compute the tool positions over a time period using Numerical Integration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing Trajectory\n",
      "........................................................................................................................................................................................................."
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import sympy as sp\n",
    "x,y,z,r,o=sp.symbols(\"x y z r theta\")\n",
    "dt=0.05  #Time difference\n",
    "t_1=[math.radians(0)]  #Theta 1\n",
    "t_2=[-0.7]  #Theta 2\n",
    "t_3=[1.5]  #Theta 4\n",
    "back_right_joint=[]\n",
    "T=T4\n",
    "x_tool=[]\n",
    "y_tool=[]\n",
    "z_tool=[]\n",
    "#Tool Velocity Matrix\n",
    "X=sp.Matrix([[100*sp.sin((2*np.pi*o)/200)*(2*np.pi)/10],[0],[100*sp.cos((2*np.pi*o)/200)*(2*np.pi)/10],[0],[0],[0]])\n",
    "X1=sp.Matrix([[-40],[0],[0],[0],[0],[0]])\n",
    "i=0\n",
    "j=0\n",
    "print(\"Computing Trajectory\")\n",
    "while i<=200:\n",
    "    if i<100:\n",
    "        X_eval=X.subs(o,i)\n",
    "    if i>100:\n",
    "        X_eval=X1.subs(o,j)\n",
    "        j+=1\n",
    "    back_right_joint.append([t_1[i],t_2[i],t_3[i]])\n",
    "    T_eval=T.subs([(theta_1,t_1[i]),(theta_2,t_2[i]),(theta_3,t_3[i])])\n",
    "    x_tool.append(T_eval[3])\n",
    "    y_tool.append(T_eval[7])\n",
    "    z_tool.append(T_eval[11])\n",
    "    J_eval=J.subs([(theta_1,t_1[i]),(theta_2,t_2[i]),(theta_3,t_3[i])])\n",
    "    q=J_eval.pinv()*X_eval\n",
    "    q=q*dt\n",
    "#     print(q)\n",
    "    t_1.append(q[0]+t_1[i])\n",
    "    t_2.append(q[1]+t_2[i])\n",
    "    t_3.append(q[2]+t_3[i])\n",
    "#     back_right_joint.append([q[0]+t_1[i],q[1]+t_2[i],q[2]+t_2[i]])\n",
    "    print(\".\",end=\"\")\n",
    "    i=i+1\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting trajectory in 2D Space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(x_tool,z_tool)\n",
    "# plt.scatter(0,0.725)\n",
    "plt.xlabel(\"y coordinate\")\n",
    "plt.ylabel(\"z coordinate\")\n",
    "plt.axis(\"equal\")\n",
    "plt.title(\"Trajectory of Robot\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Trajectory in 3D Space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.figure(figsize=(10,10)).add_subplot(projection='3d')\n",
    "# ax.axes.set_xlim3d(left=0.65, right=0.7) \n",
    "# ax.axes.set_ylim3d(bottom=-0.15, top=0.15) \n",
    "# ax.axes.set_zlim3d(bottom=0.625, top=0.825) \n",
    "ax.set_xlabel('$X$', fontsize=12)\n",
    "ax.set_ylabel('$Y$', fontsize=12)\n",
    "ax.set_zlabel('$Z$', fontsize=12)\n",
    "# ax.set_zticks(np.linspace(0.625,0.825,5).round(3))\n",
    "# ax.set_xticks(np.linspace(0.65,0.7,20).round(2))\n",
    "# ax.set_yticks(np.linspace(-0.15,0.15,5).round(2))\n",
    "ax.plot(x_tool, y_tool, z_tool, label='Tool Trajectory')\n",
    "ax.plot(np.full(len(y_tool),0.65), y_tool, z_tool, label='Projection of Circle on X Plane')\n",
    "ax.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.0, -0.7, 1.5],\n",
       " [2.63651055151647e-9, -0.704388528480752, 1.50974151642953],\n",
       " [5.35502224274068e-9, -0.708605271463034, 1.51942761882744],\n",
       " [8.15584629095369e-9, -0.712644586503961, 1.52904979081417],\n",
       " [1.10392387003570e-8, -0.716500983081226, 1.53859955836437],\n",
       " [1.40053886194929e-8, -0.720169132885286, 1.54806849135848],\n",
       " [1.70544060608232e-8, -0.723643880511664, 1.55744820542409],\n",
       " [2.01863089944900e-8, -0.726920254539753, 1.56673036410332],\n",
       " [2.34010098154557e-8, -0.729993478978365, 1.57590668138079],\n",
       " [2.66983012086349e-8, -0.732858985051743, 1.58496892460392],\n",
       " [3.00778414259415e-8, -0.735512423292964, 1.59390891782525],\n",
       " [3.35391390173786e-8, -0.737949675904533, 1.60271854559365],\n",
       " [3.70815370531595e-8, -0.740166869338657, 1.61138975721819],\n",
       " [4.07041968934821e-8, -0.742160387042182, 1.61991457152574],\n",
       " [4.44060815755597e-8, -0.743926882303588, 1.62828508212954],\n",
       " [4.81859388974953e-8, -0.745463291131795, 1.63649346322250],\n",
       " [5.20422843003810e-8, -0.746766845089030, 1.64453197590509],\n",
       " [5.59733836548901e-8, -0.747835083992636, 1.65239297505313],\n",
       " [5.99772360935308e-8, -0.748665868393693, 1.66006891672659],\n",
       " [6.40515570304344e-8, -0.749257391733725, 1.66755236611537],\n",
       " [6.81937615326149e-8, -0.749608192074804, 1.67483600601295],\n",
       " [7.24009482362443e-8, -0.749717163293099, 1.68191264580362],\n",
       " [7.66698840006551e-8, -0.749583565621591, 1.68877523094289],\n",
       " [8.09969895250282e-8, -0.749207035424420, 1.69541685290544],\n",
       " [8.53783261585535e-8, -0.748587594083264, 1.70183075956848],\n",
       " [8.98095841548310e-8, -0.747725655875504, 1.70801036599257],\n",
       " [9.42860726243473e-8, -0.746622034724776, 1.71394926555594],\n",
       " [9.88027114549430e-8, -0.745277949707037, 1.71964124139198],\n",
       " [1.03354025473152e-7, -0.743695029199545, 1.72508027807401],\n",
       " [1.07934141110443e-7, -0.741875313566262, 1.73026057348555],\n",
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       " [-8.33233329071737e-9, -0.698763946662660, 1.50328203168814]]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "back_right_joint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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